Structure-Aware Sparse-View X-ray 3D Reconstruction

• URL: http://arxiv.org/abs/2311.10959v3
• Date: Sat, 23 Mar 2024 17:36:19 GMT
• Title: Structure-Aware Sparse-View X-ray 3D Reconstruction
• Authors: Yuanhao Cai, Jiahao Wang, Alan Yuille, Zongwei Zhou, Angtian Wang,
• Abstract summary: We propose a framework, Structure-Aware X-ray Neural Radiodensity Fields (SAX-NeRF) for sparse-view X-ray 3D reconstruction. Linefomer captures internal structures of objects in 3D space by modeling the dependencies within each line segment of an X-ray. Experiments on X3D show that SAX-NeRF surpasses previous NeRF-based methods by 12.56 and 2.49 dB on novel view synthesis and CT reconstruction.
• Score: 26.91084106735878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: X-ray, known for its ability to reveal internal structures of objects, is expected to provide richer information for 3D reconstruction than visible light. Yet, existing neural radiance fields (NeRF) algorithms overlook this important nature of X-ray, leading to their limitations in capturing structural contents of imaged objects. In this paper, we propose a framework, Structure-Aware X-ray Neural Radiodensity Fields (SAX-NeRF), for sparse-view X-ray 3D reconstruction. Firstly, we design a Line Segment-based Transformer (Lineformer) as the backbone of SAX-NeRF. Linefomer captures internal structures of objects in 3D space by modeling the dependencies within each line segment of an X-ray. Secondly, we present a Masked Local-Global (MLG) ray sampling strategy to extract contextual and geometric information in 2D projection. Plus, we collect a larger-scale dataset X3D covering wider X-ray applications. Experiments on X3D show that SAX-NeRF surpasses previous NeRF-based methods by 12.56 and 2.49 dB on novel view synthesis and CT reconstruction. Code, models, and data are released at https://github.com/caiyuanhao1998/SAX-NeRF

Related papers

• fMRI-3D: A Comprehensive Dataset for Enhancing fMRI-based 3D Reconstruction [50.534007259536715]
  We present the fMRI-3D dataset, which includes data from 15 participants and showcases a total of 4768 3D objects. We propose MinD-3D, a novel framework designed to decode 3D visual information from fMRI signals.
  arXiv  Detail & Related papers  (2024-09-17T16:13:59Z)
• R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction [53.19869886963333]
  3D Gaussian splatting (3DGS) has shown promising results in rendering image and surface reconstruction. This paper introduces R2$-Gaussian, the first 3DGS-based framework for sparse-view tomographic reconstruction.
  arXiv  Detail & Related papers  (2024-05-31T08:39:02Z)
• X-Ray: A Sequential 3D Representation For Generation [54.160173837582796]
  We introduce X-Ray, a novel 3D sequential representation inspired by x-ray scans. X-Ray transforms a 3D object into a series of surface frames at different layers, making it suitable for generating 3D models from images.
  arXiv  Detail & Related papers  (2024-04-22T16:40:11Z)
• Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis [88.86777314004044]
  We propose a 3D Gaussian splatting-based framework, namely X-Gaussian, for X-ray novel view visualization. Experiments show that our X-Gaussian outperforms state-of-the-art methods by 6.5 dB while enjoying less than 15% training time and over 73x inference speed.
  arXiv  Detail & Related papers  (2024-03-07T00:12:08Z)
• Scalable 3D Reconstruction From Single Particle X-Ray Diffraction Images Based on Online Machine Learning [34.12502156343611]
  We introduce X-RAI, an online reconstruction framework that estimates the structure of a 3D macromolecule from large X-ray SPI datasets. We demonstrate that X-RAI achieves state-of-the-art performance for small-scale datasets in simulation and challenging experimental settings.
  arXiv  Detail & Related papers  (2023-12-22T04:41:31Z)
• LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields [112.62936571539232]
  We introduce a new task, novel view synthesis for LiDAR sensors. Traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel views. We use a neural radiance field (NeRF) to facilitate the joint learning of geometry and the attributes of 3D points.
  arXiv  Detail & Related papers  (2023-04-20T15:44:37Z)
• Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction [53.93674177236367]
  Cone Beam Computed Tomography (CBCT) plays a vital role in clinical imaging. Traditional methods typically require hundreds of 2D X-ray projections to reconstruct a high-quality 3D CBCT image. This has led to a growing interest in sparse-view CBCT reconstruction to reduce radiation doses. We introduce a novel geometry-aware encoder-decoder framework to solve this problem.
  arXiv  Detail & Related papers  (2023-03-26T14:38:42Z)
• Oral-3Dv2: 3D Oral Reconstruction from Panoramic X-Ray Imaging with Implicit Neural Representation [3.8215162658168524]
  Oral-3Dv2 is a non-adversarial-learning-based model in 3D radiology reconstruction from a single panoramic X-ray image. Our model learns to represent the 3D oral structure in an implicit way by mapping 2D coordinates into density values of voxels in the 3D space. To the best of our knowledge, this is the first work of a non-adversarial-learning-based model in 3D radiology reconstruction from a single panoramic X-ray image.
  arXiv  Detail & Related papers  (2023-03-21T18:17:27Z)
• 3D-aware Image Synthesis via Learning Structural and Textural Representations [39.681030539374994]
  We propose VolumeGAN, for high-fidelity 3D-aware image synthesis, through explicitly learning a structural representation and a textural representation. Our approach achieves sufficiently higher image quality and better 3D control than the previous methods.
  arXiv  Detail & Related papers  (2021-12-20T18:59:40Z)
• End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images [6.645111950779666]
  We present an end-to-end Convolutional Neural Network (CNN) approach for 3D reconstruction of knee bones directly from two bi-planar X-ray images.
  arXiv  Detail & Related papers  (2020-04-02T08:37:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.